We consider the problem of finding a common fixed point of an infinite family ${T_n}$ of nonlinear self-mappings of a closed convex subset $C$ of a real Hilbert space $H$. Namely, we want to find a point $x$ with the property (assuming such common fixed points exist): [ xin igcap_{n=1}^infty ext{Fix}(T_n). ] We will use the Krasnoselskii-Mann (KM) Type inertial iterative algorithms of the form $$ x_{n+1} = ((1-alpha_n)I+alpha_nT_n)y_n,quad y_n = x_n + eta_n(x_n-x_{n-1}).eqno(*)$$ We discuss the convergence properties of the sequence ${x_n}$ generated by this algorithm (*). In particular, we prove that ${x_n}$ converges weakly to a common fixed point of the family ${T_n}$ under certain conditions imposed on the sequences ${alpha_n}$ and ${eta_n}$.

In the paper we study sequences of random functions which are defined by some interpolation procedures for a given random function. We investigate the problem in what sense and under which conditions the sequences converge to the prescribed random function. Sufficient conditions for convergence of moment characteristics, of finite dimensional distributions and for weak convergence of distributions in spaces of continuous functions are given. The treatment of such questions is stimulated by an investigation of Monte Carlo simulation procedures for certain classes of random functions. In an appendix basic facts concerning weak convergence of probability measures in metric spaces are summarized.

We consider three problems in this dissertation, all under the unifying theme of random matrix products. The first and second problems are concerned with weak convergence in stochastic matrices and circulant matrices, respectively, and the third is concerned with the numerical calculation of the Lyapunov exponent associated with some random Fibonacci sequences. Stochastic matrices are nonnegative matrices whose row sums are all equal to 1. They are most commonly encountered as transition matrices of Markov chains. Circulant matrices, on the other hand, are matrices where each row after the first is just the previous row cyclically shifted to the right by one position. Like stochastic matrices, circulant matrices are ubiquitous in the literature.In the first problem, we study the weak convergence of the convolution sequence mu to the n, where mu is a probability measure with support S sub mu inside the space S of d by d stochastic matrices, d greater than or equal to 3. Note that mu to the n is precisely the distribution of the product X sub 1 times X sub 2 times and so on times X sub n of the mu distributed independent random variables X sub 1, X sub 2, and so on, X sub n taking values in S. In [CR] Santanu Chakraborty and B.V. Rao introduced a cyclicity condition on S sub mu and showed that this condition is necessary and sufficient for mu to the n to not converge weakly when d is equal to 3 and the minimal rank r of the matrices in the closed semigroup S generated by S sub mu is 2. Here, we extend this result to any d bigger than 3. Moreover, we show that when the minimal rank r is not 2, this result does not always hold.The second problem is an investigation of weak convergence in another direction, namely the case when the probability measure mu's support S sub mu consists of d by d circulant matrices, d greater than or equal to 3, which are not necessarily nonnegative. The resulting semigroup S generated by S sub mu now lacking the nice property of compactness in the case of stochastic matrices, we assume tightness of the sequence mu to the n to analyze the problem. Our approach is based on the work of Mukherjea and his collaborators, who in [LM] and [DM] presented a method based on a bookkeeping of the possible structure of the compact kernel K of S.The third problem considered in this dissertation is the numerical determination of Lyapunov exponents of some random Fibonacci sequences, which are stochastic versions of the classical Fibonacci sequence f sub (n plus 1) equals f sub n plus f sub (n minus 1), n greater than or equal to 1, and f sub 0 equal f sub 1 equals 1, obtained by randomizing one or both signs on the right side of the defining equation and or adding a "growth parameter." These sequences may be viewed as coming from a sequence of products of i.i.d. random matrices and their rate of growth measured by the associated Lyapunov exponent. Following techniques presented by Embree and Trefethen in their numerical paper [ET], we study the behavior of the Lyapunov exponents as a function of the probability p of choosing plus in the sign randomization.

Darbe gaunama, jog silpno matų konvergavimo erdvės X prasme išlpaukia charakteringųjų transformacijų konvergavimas ir atvirkščiai, jeigu charakteringosios transformacijos konverguoja į funkcijas tolydžias nuliniame taške, tai iš čia išplaukia matų silpnas konvergavimas erdvės X prasme. / It is obtained, that the weak convergence in the sense of X implies the convergence of characteristic transforms, and, on the contrary, if the characteristic transforms converge weakly to the functions contiuous at zero, the from this the weak convergence in the sense of X for the probability measures fallows.

Darbe įrodyta diskreti ribinė teorema bendrųjų Dirichlė eilučių poklasiui meromorfinių funkcijų erdvėje. Pateiktas ribinio mato išreikštinis pavidalas. / The discrete limit theorem for general Dirichlet series in the space of meromorphic functions was proved in this paper. Expressed shape of limit measue was provided.

Published in Lietuvos Matematikos Rinkinys, Vol. 46, No. 3, pp. 332–350, July–September, 2006.

Dans la thèse on introduit et on étudie une généralisation spatiale sur $\R^d$ du quantile réel usuel sous la forme d'une surface quantile via des formes $\phi$ et d'un point d'observation $O$. Notre point de départ est de simplement admettre la subjectivité due à l'absence de relation d'ordre totale dans $\R^d$ et donc de développer une vision locale et directionnelle des données. Ainsi, les observations seront ordonnées du point de vue d'un observateur se trouvant à un point $O \in \R^d$. Dans le chapitre 2, on introduit la notion du quantile vue d'un observateur $O$ dans la direction $u \in \Sd$ et de niveau $\alpha$ via des des demi-espaces orthogonaux à chaque direction d'observation. Ce choix de classe implique que les résultats de convergence ne dépendent pas du choix de $O$. Sous des hypothèses minimales de régularité, l'ensemble des points quantile vue de $O$ définit une surface fermée. Sous hypothèses minimales, on établit pour les surfaces quantile empiriques associées les théorèmes limites uniformément en le niveau de quantile et la direction d'observation, avec vitesses asymptotiques et bornes d'approximation non-asymptotiques. Principalement la LGNU, la LLI, le TCLU, le principe d'invariance fort uniforme puis enfin l'approximation du type Bahadur-Kiefer uniforme, et avec vitesse d'approximation. Dans le chapitre 3, on étend les résultats du chapitre précédent au cas où les formes $\phi$ sont prises dans une classe plus générale (fonctions, surfaces, projections géodésiques, etc) que des demi-espaces qui correspondent à des projections orthogonales par direction. Dans ce cadre plus général, les résultats dépendent fortement du choix de $O$, et c'est ce qui permet de tirer des interprétations statistiques. Dans le chapitre 4, des conséquences méthodologiques en statistique inférentielle sont tirées. Tout d'abord on introduit une nouvelle notion de champ de profondeurs directionnelles baptisée champ d'altitude. Ensuite, on définit une notion de distance entre lois de probabilité, basée sur la comparaison des deux collections de surfaces quantile du type Gini-Lorrentz. La convergence avec vitesse des mesures empiriques pour cette distance quantile, permet de construire différents tests en contrôlant leurs niveaux et leurs puissances. Enfin, on donne une version des résultats dans le cas où une information auxiliaire est disponible sur une ou plusieurs coordonnées sous la forme de la connaissance exacte de la loi sur une partition finie. / The main issue of the thesis is the development of spatial generalizations on $\R^d$ of the usual real quantile. Facing the usual fact that $\R^d$ is not naturally ordered, our idea is to simply admit subjectivity and thus to define a local viewpoint rather than a global one, anchored at some point of reference $O$ and arbitrary shape $\phi$ with the motivation of crossing information gathered by changing viewpoint $O$, shape $\phi$ and $\alpha$-th order of quantile. In Chapter 2, we study the spatial quantile points seen from an observer $O$ in a direction $u \in \Sd$ of level $\alpha$ through the class of the half-spaces orthogonal to the direction $u$. This choice implies that the convergence theorems do not depend on the choice of $O$. Under minimal regularity assumptions, the set of all quantile points seen from $O$ is a closed surface. Under minimal assumptions, we establish for the associated empirical quantile surfaces the convergence theorems uniformly on the quantile level and the observation direction with the asymptotic speed and non-asymptotic bounds of approximation. Mainly, we establish the ULLN, the ULIL, the UCLT, the uniform strong invariance principle and finally the Bahadur-Kiefer type embedding, with the approximation speed rate. In Chapter 3, all the results of the previous chapter are extended to the case where the shapes $ \phi $ are taken in a class more general (functions, surfaces, geodesic projections, etc) than orthogonal projections (half-spaces). In this general setting, the results depend strongly on the choice of $ O $. It is this dependence which permit to draw statistical interpretations: modes detection, mass localization, etc. In Chapter 4, some methodological consequences in inferential statistics are drawn. First we introduce a new concept of directional depth fields called altitude fields. In a second application is defined a new distances between probability distributions, based on the comparison of two collections of quantile surfaces, which are indexes of the type Gini-Lorrentz. The convergence with speed of the empirical quantile measures for these distances, can build different tests with control of their level and their power. A third use of the quantile surfaces is for the case where $ \alpha = 1/2$. Finally, we give a version of our theorems in the case where auxiliary information is available on one or more coordinates of the random variable. By assuming known the probability of the elements of a finite partition, the asymptotic variance of the limiting process decreases and the simulations with few points clearly shows the reframe of the estimated surfaces to the real ones.

We study weak convergence of sequences in Banach spaces. In particular, we compare the notions of weak and norm convergence. Although these modes of convergence usually differ, we show that in ℓ¹ they coincide. We then show a theorem of Rosenthal's which states that if {𝓍ₙ} is a bounded sequence in a Banach space, then {𝓍ₙ} has a subsequence {𝓍'ₙ} satisfying one of the following two mutually exclusive alternatives; (i) {𝓍'ₙ} is weakly Cauchy, or (ii) {𝓍'ₙ} is equivalent to the unit vector basis of ℓ¹.

The study of stochastic queueing networks is quite important due to the many applications including transportation, telecommunication, and manufacturing industries. Since there is often no explicit solution to these types of control problems, numerical methods are needed. Following the method of Boué-Dupuis, we use a Dynamic Programming approach of optimization on a controlled Markov Chain that simulates the behavior of a fluid limit of the original process. The search for an optimal control in this case involves a Skorokhod problem to describe the dynamics on the boundary of closed, convex domain. Using relaxed stochastic controls we show that the approximating numerical solution converges to the actual solution as the size of the mesh in the discretized state space goes to zero, and illustrate with an example. / Ph. D.

A sequence fxng generates by the formula x_{n+1} =(1- £\\_n)x_n+ £\\_nT_nx_n is called the Krasnosel'skii-Mann algorithm, where {£\\_n} is a sequence in (0,1) and {T_n} is a sequence of nonexpansive mappings. We introduce KM algorithm and prove that the sequence fxng generated by KM algorithm converges weakly. This result is used to solve the split feasibility problem which is to find a point x with the property that x ∈ C and Ax ∈ Q, where C and Q are closed convex subsets form H1 to H2, respectively, and A is a bounded linear operator form H1 to H2. The purpose of this paper is to present some results which apply KM algorithm to solve the split feasibility problem, the multiple-set split feasibility problem and other applications.